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Robust GP in robot learning

  • Applications of Evolutionary Computation Evolutionary Computation in Machine Learning, Neural Networks, and Fuzzy Systems
  • Conference paper
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Parallel Problem Solving from Nature — PPSN IV (PPSN 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1141))

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Abstract

This paper presents a new approach to Genetic Programming (i.e. GP). Our goal is to realize robustness by means of the automatic discovery of functions. In traditional GP, techniques have been proposed which attempt to discover certain subroutines for the sake of improved efficiency. So far, however, the robustness of GP has not yet been discussed in terms of knowledge acquisition. We propose an approach for robustness named COAST, which has a library for storing certain subroutines for reuse. We make use of the Wall Following Problem to illustrate the efficiency of this method.

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Reference

  1. Kinnear, K.: Alternatives in Automatic Function Definition: A Comparison of Performance in Advances in Genetic Programming, K. E. Kinnear, Jr., Ed. Cambridge, MA: MIT Press.

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Author information

Authors and Affiliations

  1. Complex Systems Engineering, Division of Systems and Information Engineering, Hokkaido University, N-13 W-8, Sapporo, 060, Hokkaido, Japan

    Naohiro Hondo & Yukinori Kakazu

  2. Machine Inference Section, Electrotechnical Lab., 1-1-4 Umezono, Tukuba Science City, 305, Ibaraki, Japan

    Hitoshi Iba

Authors
  1. Naohiro Hondo
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  2. Hitoshi Iba
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  3. Yukinori Kakazu
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Editor information

Hans-Michael Voigt Werner Ebeling Ingo Rechenberg Hans-Paul Schwefel

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© 1996 Springer-Verlag Berlin Heidelberg

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Hondo, N., Iba, H., Kakazu, Y. (1996). Robust GP in robot learning. In: Voigt, HM., Ebeling, W., Rechenberg, I., Schwefel, HP. (eds) Parallel Problem Solving from Nature — PPSN IV. PPSN 1996. Lecture Notes in Computer Science, vol 1141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61723-X_1038

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  • DOI: https://doi.org/10.1007/3-540-61723-X_1038

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61723-5

  • Online ISBN: 978-3-540-70668-7

  • eBook Packages: Springer Book Archive

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